Prediction of activity-related energy expenditure under free-living conditions using accelerometer-derived physical activity
The purpose of the study was to develop prediction models to estimate physical activity (PA)-related energy expenditure (AEE) based on accelerometry and additional variables in free-living adults. In 50 volunteers (20-69 years) PA was determined over 2 weeks using the hip-worn Actigraph GT3X + as ve...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
04 October 2022
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| In: |
Scientific reports
Year: 2022, Volume: 12, Pages: 1-11 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-022-20639-0 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41598-022-20639-0 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41598-022-20639-0 
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| Author Notes: | Stephanie Jeran, Astrid Steinbrecher, Verena Haas, Anja Mähler, Michael Boschmann, Klaas R. Westerterp, Boris A. Brühmann, Karen Steindorf &Tobias Pischon |
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| 520 | |a The purpose of the study was to develop prediction models to estimate physical activity (PA)-related energy expenditure (AEE) based on accelerometry and additional variables in free-living adults. In 50 volunteers (20-69 years) PA was determined over 2 weeks using the hip-worn Actigraph GT3X + as vector magnitude (VM) counts/minute. AEE was calculated based on total daily EE (measured by doubly-labeled water), resting EE (indirect calorimetry), and diet-induced thermogenesis. Anthropometry, body composition, blood pressure, heart rate, fitness, sociodemographic and lifestyle factors, PA habits and food intake were assessed. Prediction models were developed by context-grouping of 75 variables, and within-group stepwise selection (stage I). All significant variables were jointly offered for second stepwise regression (stage II). Explained AEE variance was estimated based on variables remaining significant. Alternative scenarios with different availability of groups from stage I were simulated. When all 11 significant variables (selected in stage I) were jointly offered for stage II stepwise selection, the final model explained 70.7% of AEE variance and included VM-counts (33.8%), fat-free mass (26.7%), time in moderate PA + walking (6.4%) and carbohydrate intake (3.9%). Alternative scenarios explained 53.8-72.4% of AEE. In conclusion, accelerometer counts and fat-free mass explained most of variance in AEE. Prediction was further improved by PA information from questionnaires. These results may be used for AEE prediction in studies using accelerometry | ||
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